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Kanal tas-sors @linuxgram · Post #17841 · Fra 19

📰 AI Helped Uncover A "50-80x Improvement" For Linux's IO_uring Linux block maintainer and IO_uring lead developer Jens Axboe recently was debugging some slowdowns in the AHCI/SCSI code with IO_uring usage. When turning to Claude AI to help in sorting through the issue, patches were devised that can deliver up to a "literally yield a 50-80x improvement on the io_uring side for idle systems." The code is on its way to the Linux kernel... 🔗 Source: https://www.phoronix.com/news/AI-50-80x-IO-uring #linux#kernel

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djangoproject

@djangoproject · Post #118 · 08/08/2016 11:44

https://docs.python.org/3/library/multiprocessing.html multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows. The #multiprocessing module also introduces #APIs which do not have analogs in the #threading#module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data #parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,